Extracellular Vesicle Mobility in Collagen I Hydrogels Is Influenced by Matrix-Binding Integrins
Bibliographic record
Abstract
Extracellular vesicles (EVs) are a diverse population of membrane structures produced and released by cells into the extracellular space for the intercellular trafficking of cargo molecules. They are implicated in various biological processes, including angiogenesis, immunomodulation, and cancer cell signaling. While much research has focused on their biogenesis or their effects on recipient cells, less is understood about how EVs are capable of traversing diverse tissue environments and crossing biological barriers. Their interactions with extracellular matrix components are of particular interest, as such interactions govern diffusivity and mobility, providing a potential basis for organotropism. To start to untangle how EV-matrix interactions affect diffusivity, we use high speed epifluorescence microscopy, single particle tracking, and confocal reflectance microscopy to analyze particle mobility and localization in extracellular matrix-mimicking hydrogels composed of collagen I. EVs are compared with synthetic liposomes and extruded plasma membrane vesicles to better understand the importance of membrane composition on these interactions. By treating EVs with trypsin to digest surface proteins, we determine that proteins are primarily responsible for EV immobilization in collagen I hydrogels. We next use a synthetic peptide competitive inhibitor to narrow down the identity of the proteins involved to argynylglycylaspartic acid (RGD) motif-binding integrins, which interact with unincorporated or denatured nonfibrillar collagen. Moreover, the effect of integrin inhibition with RGD peptides has strong implications for the use of RGD-peptide-based drugs to treat certain cancers, as integrin inhibition appears to increase EV mobility, improving their ability to infiltrate tissue-like environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".